agentic-context-engine: Skill for Claude Code

.claude/skills/kayba-pipeline/stage-7-fixer/SKILL.md

kayba-stage-7-fixer is a skill for Claude Code from kayba-ai/agentic-context-engine. It costs 61 tokens per session (1,765 once invoked), scanned A, original, Apache-2.0.

A process for implementing approved fixes from an action plan and recording what changed. It uses evaluation files that describe the plan, decisions, and before-fix measurements.

In plain words
What is it for?
Use it when asked to run stage 7, implement fixes, or apply an approved action plan that includes the required evaluation files.
Why use it?
It creates a rollback checkpoint and change log before edits, making the fix work traceable and reversible.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is kayba-ai/agentic-context-engine's own configuration. It tells Claude Code how to work on agentic-context-engine itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agentic-context-engine configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python eval/compute_baselines.py --traces-dir <new_traces_folder> --output eval/post_fix_metrics.json.

About the project

Agentic Context Engine is an open-source engine that gives AI agents a persistent learning loop, helping them remember successful strategies and learn from failures across sessions. It is used to improve production agents, and also powers Kayba’s hosted service. Catalogue add-ons support workflows for operating and configuring the engine.

kayba-ai/agentic-context-engine · 2,565 stars · on GitHub · kayba.ai

Reuse

Borrowing it

Nothing to install: this file belongs to kayba-ai/agentic-context-engine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/kayba-ai/agentic-context-engine/main/.claude/skills/kayba-pipeline/stage-7-fixer/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/kayba-ai/agentic-context-engine

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for kayba-stage-7-fixer

README.md
[![agentmods](https://agentmods.dev/badge/skills/kayba-ai/agentic-context-engine/stage-7-fixer.svg)](https://agentmods.dev/skills/kayba-ai/agentic-context-engine/stage-7-fixer)
Your own site
<a href="https://agentmods.dev/skills/kayba-ai/agentic-context-engine/stage-7-fixer"><img src="https://agentmods.dev/badge/skills/kayba-ai/agentic-context-engine/stage-7-fixer.svg" alt="Measured on agentmods" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,765 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00061 $0.01765
Opus 5 $0.00030 $0.00882
Sonnet 5 $0.00012 $0.00353
Haiku 4.5 $0.00006 $0.00177

Measured 9d ago against content hash 08b8439bd785, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

kayba-stage-7-fixer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/kayba-pipeline/stage-7-fixer/SKILL.md · 192 lines

How it starts

The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Stage 7: Fix Implementation

Implement every non-discarded fix from the approved action plan.

Inputs

  • eval/action_plan.md -- the approved action plan from Stage 5 (possibly modified during HITL in Stage 6)
  • eval/stage6_decision.md -- if it exists, the HITL decision record from Stage 6 (contains user modifications)
  • eval/baseline_metrics.json -- the pre-fix baseline metrics from Stage 3 (for reference in changes log)

Read the action plan and stage6 decision (if present) before starting.

Pre-flight: Git Safety Checkpoint

Before making ANY changes to source files:

  1. Run git status to confirm the working tree state
  2. Create a safety commit or stash:
    git stash push -m "pre-pipeline-fixes-$(date +%Y%m%d-%H%M%S)"
    
    If there are no uncommitted changes to stash, create a lightweight tag instead:
    git tag pre-pipeline-fixes-$(date +%Y%m%d-%H%M%S)
    
  3. Record the stash ref or tag name in eval/changes_log.md under a "Rollback" section so the user can restore if needed

This ensures every fix is reversible with a single git stash pop or git checkout.

Pre-flight: HITL Modification Check

If eval/stage6_decision.md exists:

  1. Read it and identify any items the user modified, added, or re-prioritized during Stage 6
  2. Build a set of HITL_MODIFIED_IDS -- the insight/skill IDs that the user changed
  3. When logging each fix later, tag modified items with [HITL-MODIFIED] in the changes log so reviewers know which fixes reflect user judgment vs. the original pipeline output

If the file does not exist, assume no HITL modifications were made.

Pre-flight: Conflict Scan

Before implementing any fixes, scan the action plan for potential conflicts:

  1. Build a map of file_path -> [fix IDs that touch it]
  2. If two or more fixes modify the same file, flag them as co-located
  3. If two or more fixes modify the same section (within ~20 lines of each other), flag them as overlapping
  4. For overlapping fixes: plan to apply them sequentially in priority order, re-reading the file between each edit to ensure the second fix still makes sense on top of the first
  5. Log any detected conflicts at the top of eval/changes_log.md under a "Conflict Notes" section

Read the full file on GitHub · 192 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 192 lines · 61 tokens per session scan A 08b8439bd785

Subscribe to this mod's changes

kayba-stage-7-fixer is a skill published in the GitHub repository kayba-ai/agentic-context-engine (2,565 stars, last pushed 10d ago), licensed Apache-2.0. It adds 61 tokens to every session and 1,765 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

titen-memory

Use the Titen MCP server to recall bounded evidence-grounded context, record verified durable signals, submit feedback, and coordinate checkpoints, leases, or handoffs. Use when work may benefit from prior project memory or when a verified outcome should be preserved for another agent; do not use it to capture raw…

RamaAditya49/titen · 77 tokens

open-source

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…

browser-use/browser-use · 137 tokens

writing

A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.

bojieli/ai-agent-book · 27 tokens

mnemon

Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.

mnemon-dev/mnemon · 25 tokens

goai

GoAI is a Go SDK for AI applications. One unified API across 25+ LLM providers. Inspired by the Vercel AI SDK, adapted to Go idioms (generics, interfaces, channels).

zendev-sh/goai · 0 tokens

mnemo-cortex

Installs and wires Mnemo Cortex (local-first persistent memory) into OpenClaw and other MCP-capable agents. Use for cross-session recall, decision history, or multi-agent shared memory.

GuyMannDude/mnemo-cortex · 44 tokens